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Although remarkable improvement has been achieved in stretchable strain sensors, challenges still exist in aspects including intelligent sensing, simultaneous data processing, and scalable fabrication techniques. In this work, a strain-sensitive device is presented by fabricating a CsPbBr3 quantum dots (QDs) floating-gate field-effect transistor (FET) sensing array on thin polyimide (PI) films. The FET exhibits an excellent on/off ratio (>103) and a large memory window (>2 V). With the introduction of CsPbBr3 QDs as the trapping layer, an additional UV response is obtained because of the photogenerated charge carriers that significantly enhance the source-drain current (IDS) of the device. At each electrical state, the IDS varies with the strains and the sensing range is from compressive +12.5% to tensile -10.8%. Excellent data retainability and mechanical durability demonstrate the high quality and reliability of the fabricated sensors. Furthermore, synapse functions including long-term potentiation (LTP), long-term depression (LTD), etc., are emulated at the device level. Linearity factor changes of LTP/LTD in different sensing scenarios demonstrate the reliability of the device and further confirm the different sensing mechanisms with/without UV illumination. Our results exhibit the potential of transistor-based devices for multifunctional intelligent sensing.

It is of great significance to develop multifunction-integrated photoelectric synaptic devices with the simple structure to imitate the functions of eyes. In this work, we reported the multimodal photoelectric neuromorphic thin film transistors (TFTs) with single gate and two input terminals using sorted semiconducting single-walled carbon nanotubes (sc-SWCNTs) mixed with lead-free perovskite (CsBi3I10) and lightly n-doped silicon as active layers and gate electrodes. The results showed that optical and electrical responses of SWCNT TFTs could increase 26 and 23 times after addition of CsBi3I10 under the same pulse sequence stimulation, respectively. Apart from emulating a series of typical synaptic functionalities, an artificial neural network based on SWCNT phototransistors was simulated to be trained for the recognition of handwritten digits in the Modified National Institute of Standards and Technology with the maximum accuracy of about 85.46%. Moreover, our synaptic devices can imitate traditional Pavlovian conditioning, and show NOR logic, reproducible flash memory functions under the optical and electrical programming stimulations. It is noted that the erasing voltage (3 V, 0.2 s) is one of the best value among the reported phototransistor memory. Furthermore, our synaptic devices can work well after more than 150 programming/erasing operation cycles and over 40 days testing.

This article provides a review of current development and challenges in memristor modeling. We review the mechanisms of memristive devices based on various classifications and survey the progress of memristive models and simulations. Except for classical models in theory, different modeling architectures are compared, including first-principle, modular dynamics and the finite element tools like COMSOL and MATLAB. Challenges and strategies for memristors with non-ideal mechanisms, including large parameter variations, modeling algorithms and simulation roadblocks are also discussed.

Recently, mainstream silicon (Si)-based materials and complementary metal oxide semiconductor (CMOS) technology have been used in developing extremely tiny sized (of a few nanometers) devices. However, with the reduction of transistor characteristic dimensions, many new challenges such as the short channel effect and high heat dissipation problems have emerged. Two-dimensional transition metal dichalcogenides (2D TMDs) are deemed the most promising semiconductor materials to conquer the challenge of the short channel effect owing to their excellent properties, including high mobility and atomic thickness. Nevertheless, Fermi-level pinning (FLP) occurs when TMDs are in direct contact with metal electrodes, which causes an uncontrollable Schottky barrier and a high contact resistance, limiting the device performance. In this review, we summarize the recent progress on how to circumvent FLP between 2D TMDs semiconductors and metals. Firstly, the related concepts, aiming to get an in-depth understanding of FLP are introduced. Secondly, we discuss the factors contributing to FLP in detail and the strategies of Fermi-level depinning according to these factors. Finally, we present a summary and outlook, which will provide a guideline for suppressing FLP in the process of fabricating high-performance 2D TMD devices.

Resistive random access memories (RRAMs) based on the electrochemical metallization mechanism (ECM) have potential applications in high-density data storage and efficient neuromorphic computing. However, the high variability of ECM devices still hinders their application in artificial intelligence owing to the random formation of conductive filaments (CFs). Here, we demonstrate 2D covalent organic framework (COF) RRAM with electroforming-free resistive switching behavior, low spatial/temporal variations, and excellent retention capability up to 105 s. The one-dimensional channels of the oriented COF-5 film can not only confine the shape of filaments but also modulate the transition direction of Ag ions. Moreover, alcohol vapors could activate the device to achieve gas-mediated multilevel resistive switching since COF materials can absorb small molecules through host guest interactions to vary the conductivity. An alcohol gas recognition system constructed by integrating the COF RRAM as a sensor and filter part with the k-nearest neighbors (KNN) algorithm as a classifier was demonstrated with a recognition accuracy of 87.2%. Furthermore, the effect of alcohol inhibition stimulation in the human nervous system is successfully emulated by the COF RRAM.

The saturation of Moore's law and the finality of Dennard scaling highlight the need for new data-storage approaches employing different physical mechanisms. Due to the low operation voltage, multibit storage and cost-effective manufacturability, memristive devices are considered to be one of the most attractive alternatives to conventional silicon-based memory. Typically, memristive devices can be switched to a low resistance state or a high resistance state by electrical SET/RESET operations, respectively. Recent years have witnessed a surge of research interest in optoelectronic memories, where tunable switching characteristics, coexistence of volatile and non-volatile behaviours, multilevel programming capability and programming/erasing steps can be achieved optically. In this work we introduce an electro-photoactive memristive device based on natural melanin with favorable electrical and optical properties. The device shows reproducible bipolar resistive switching with a low SET voltage (0.2 V) and an ON/OFF ratio over 104. The SET voltage can be easily tuned from 0.2 V to 1.2 V by varying the wavelength or the intensity of the optical input. Due to this phototunable switching characteristic, light-driven switchable NAND and NOR operations have been realized on a melanin-based circuit.

With the miniaturization of silicon-based electronic components, power consumption is becoming a fundamental issue for micro-nano electronic circuits. The main reason for this is that the scaling of the supply voltage in the ultra-large-scale integrated circuit cannot keep up with the shrinking of the characteristic size of conventional transistors due to the physical limit termed "Boltzmann Tyranny", in which a gate voltage of at least 60 mV is required to modulate the drain current by one order of magnitude. Accordingly, to solve this problem, several new transistor architectures have been designed to reduce the subthreshold swing (SS) to lower than the fundamental limitation, thus lowering the supply voltage and reducing the power consumption. In this review, we first analytically formulate the SS, summarize the methods for reducing the SS, and propose four new transistor concepts, including tunnelling field-effect transistor, negative capacitance field-effect transistor, impact ionization field-effect transistor, and cold source field-effect transistor. Then, we review their physical mechanisms and optimization methods and consider the potential and drawbacks of these four new transistors. Finally, we discuss the challenges encountered in the investigation of these steep-slope transistors and present the future outlook.

Resistive random access memory (RRAM) based on hybrid organic–inorganic halide perovskite (HOIP) materials has recently gained significant interest due to its low activation energy of ion migration. HOIP RRAM has been reported to exhibit outstanding performance, but they still suffer from issues of high operating voltage and poor uniformity. Here, we provide an efficient method for improving the resistive switching characteristics of HOIP RRAM by integrating an Au tip in the bottom electrode to regulate the growth of conductive filaments (CFs). The Ag/polymethylmethacrylate/CH3NH3PbI3/Au tip/indium tin oxide RRAM exhibits a lower set voltage of −0.11 V, a larger ON/OFF ratio of 108, excellent durability (up to 4 × 103 times), low power consumption, and good non-volatility. Moreover, the time-dependent resistive switching characteristics were investigated by a statistical analysis of the data on the turn-on time based on the Weibull distribution. Tests using transmission electron microscopy, electron energy depletion spectroscopy, and energy dispersive spectroscopy have demonstrated that the formation and rupture of CFs occur mainly in the CH3NH3PbI3 layer and that the CFs should be composed of Ag atoms and I vacancies. Our research offers a strategy for enhancing the stability of perovskite devices and promoting the application of low-power, high-density non-volatile memory devices.

Neuromorphic computing inspired by the functions of human brain has attracted much attention in the research area of artificial intelligence. Herein, we report an optoelectronic synapse based on a polyvinylpyrrolidone (PVPy)-upconverting nanoparticle (UCNP) hybrid floating gate transistor. The UCNP-based synaptic transistor exhibits a remarkable near-infrared (NIR)-induced de-trapping behaviour as well as a long retention time. Furthermore, our device is employed to successfully emulate various synaptic functions including postsynaptic current with different duration times and pulse amplitudes, paired-pulse facilitation/depression, transition from short-term plasticity to long-term plasticity and learning-forgetting-relearning processes. Overall, this work may open up a new opportunity for NIR optoelectronic synapses.

The continued growth in the demand of data storage and processing has spurred the development of high-performance storage technologies and brain-inspired neuromorphic hardware. Semiconductor quantum dots (QDs) offer an appealing option for these applications since they combine excellent electronic/optical properties and structural stability and can address the requirements of low-cost, large-area, and solution-based manufactured technologies. Here, we focus on the development of nonvolatile memories and neuromorphic computing systems based on QD thin-film solids. We introduce recent advances of QDs and highlight their unique electrical and optical features for designing future electronic devices. We also discuss the advantageous traits of QDs for novel and optimized memory techniques in both conventional flash memories and emerging memristors. Then, we review recent advances in QD-based neuromorphic devices from artificial synapses to light-sensory synaptic platforms. Finally, we highlight major challenges for commercial translation and consider future directions for the postsilicon era.

The substantial amount of data generated every second in the big data age creates a pressing requirement for new and advanced data storage techniques. Luminescent nanomaterials (LNMs) not only possess the same optical properties as their bulk materials but also have unique electronic and mechanical characteristics due to the strong constraints of photons and electrons at the nanoscale, enabling the development of revolutionary methods for data storage with superhigh storage capacity, ultra-long working lifetime, and ultra-low power consumption. In this review, we investigate the latest achievements in LNMs for constructing next-generation data storage systems, with a focus on optical data storage and optoelectronic data storage. We summarize the LNMs used in data storage, namely upconversion nanomaterials, long persistence luminescent nanomaterials, and downconversion nanomaterials, and their applications in optical data storage and optoelectronic data storage. We conclude by discussing the superiority of the two types of data storage and survey the prospects for the field.

Antimonene, which is similar to two-dimensional (2D) phosphorene, has recently gained considerable attention because of its thickness-dependent energy band structure. However, unlike phosphorene, undoped antimonene has an indirect bandgap only at the monolayer limit. In this work, an electrochemical sodium doping strategy was proposed to tune the energy band structure of antimonene. First-principles calculations indicated that a direct bandgap of 0.88 eV formed in 5.55% Na-doped antimonene, while undoped antimonene had an indirect bandgap of 2.38 eV. Optical and electrical measurements provided clear evidence for such a reconstruction of the energy band. We experimentally demonstrated p-type conduction in antimonene quantum dots (QDs)-based field-effect transistors. Furthermore, the induced direct bandgap enabled electric-field control of the surface-enhanced Raman scattering on plasmonic-free antimonene QDs. This allowed for detection of rhodamine 6G with a detection limit down to the sub-femtomolar level. Our study highlights the potential of doped antimonene as an emerging 2D semiconductor.

Natural biomaterials are potential candidates for the next generation of green electronics due to their biocompatibility and biodegradability. On the other hand, the application of biocomposite systems in information storage, photoelectrochemical sensing, and biomedicine has further promoted the progress of environmentally benign bioelectronics. Here, we mainly review recent progress in the development of biocomposites in data storage, focusing on the application of biocomposites in resistive random-access memory (RRAM) and field effect transistors (FET) with their device structure, working mechanism, flexibility, transient characteristics. Specifically, we discuss the application of biocomposite-based non-volatile memories for simulating biological synapse. Finally, the application prospect and development potential of biocomposites are presented., Graphical abstract

Inexpensive and flexible organic resistive memory can be easily fabricated, scaled and stacked. Owing to these advantages, organic resistive memory is considered as a very promising technology capable of substituting current inorganic semiconductor-based memory technology. It has attracted a lot of attention from the scientific and engineering communities. In this mini-review, the recent state-of-the-art developments related to organic small molecules for resistive random-access memory (RRAM) have been emphasized. We first describe the general characteristics of RRAM devices, their typical structures, fabrication, classification, and switching mechanisms. Next, we discuss the usage of RRAM devices in next-generation computing. In particular, we discuss the use of organic small molecules and their composites in RRAM with various functionalities. Finally, the current challenges and developing trends of performance improvement for organic small molecule-based RRAM are discussed.

Memristor devices have been of great interest over the past decade for the implementation of brain-inspired memory and computing owing to their inherent multistage memory, exquisite structure and higher integration. Herein, a CsPbBr3 QD-based protocol was constructed to exhibit analog memristive characteristics. The field-driven charge trapping/detrapping process was accelerated in the highlighted asymmetric electrode configuration to enable persistent dual direction current modulation, which serves as a basis for synaptic weight variation in the human brain. Significantly, the synaptic functions of long-term potentiation (LTP), long-term depression (LTD) and spike-timing-dependent plasticity (STDP) have been emulated on the device level. Furthermore, light signal-facilitated paired-pulse facilitation (PPF) behavior was validated with in situ atomic force microscopy (AFM) based on electrical techniques. These results may pave a new way to produce memristive devices with advanced implications for future neuromorphic computing.

Memristors that possess massive memory capacity and outstanding processing efficiency concurrently are vitally important to surmount the limitation of Moore's law and traditional Von Neumann bottleneck. Specifically, memristors are capable of integrating various functionalities such as information storage and processing into just one unit to obtain high electrical performance and thereby satisfy the contemporary needs. Here, a multifunctional polymer memristor with combined data storage and processing abilities on the basis of novel solution-processed poly(9,9-dioctyl-9H-fluorene)-alt-1,3-bis(2-ethylhexyl)-(5,7-di(thiophen-2-yl)-4H,8H-benzo[1,2-c:4,5-c′]dithiophene-4,8-dione)-alt-((2,4-pentanedionato)bis(2-(thiophen-2-yl)-pyridine)iridium) (PFTBDD-IrTPy) is demonstrated for the first time. As confirmed by field-emission scanning electron microscopy (FE-SEM), energy dispersive X-ray spectroscopy (EDX) as well as density functional theory (DFT) calculations, the memristive behavior of the multifunctional device is attributed to the synergetic electrochemical metallization and charge transfer effect. Associated with these cation regulating and charge transfer characteristics, our polymer memristor is capable of achieving diverse functionalities comprising multilevel data storage, biological synaptic emulation, Boolean logic function as well as basic arithmetic operations and commutative law of addition, subtraction, multiplication and division. These experimental results will accelerate the development of metal complex polymer based memristors for various electronic applications.

The lobula giant movement detector (LGMD) is the movement-sensitive, wide-field visual neuron positioned in the third visual neuropile of lobula. LGMD neuron can anticipate collision and trigger avoidance efficiently owing to the earlier occurring firing peak before collision. Vision chips inspired by the LGMD have been successfully implemented in very-large-scale-integration (VLSI) system. However, transistor-based chips and single devices to simulate LGMD neurons make them bulky, energy-inefficient and complicated. The devices with relatively compact structure and simple operation mode to mimic the escape response of LGMD neuron have not been realized yet. Here, the artificial LGMD visual neuron is implemented using light-mediated threshold switching memristor. The non-monotonic response to light flow field originated from the formation and break of Ag conductive filaments is analogue to the escape response of LGMD neuron. Furthermore, robot navigation with obstacle avoidance capability and biomimetic compound eyes with wide field-of-view (FoV) detection capability are demonstrated., Development of real-time sensing capability in artificial vision system requires an integration that allow sensing, computation, and storage, whilst remain energy efficient and compact. Here, the authors mimic the lobula giant movement detector to achieve this objective via light-mediated threshold switching memristor.

Two-dimensional MXene has enormous potential for application in industry and academia owing to its surface hydrophilicity and excellent electrochemical properties. However, the application of MXene in optoelectronic memory and logical computing is still facing challenges. In this study, an optoelectronic resistive random access memory (RRAM) based on silver nanoparticles (Ag NPs)@MXene-TiO2 nanosheets (AMT) was prepared through a low-cost and facile hydrothermal oxidation process. The fabricated device exhibited a typical bipolar switching behavior and controllable SET voltage. Furthermore, we successfully demonstrated a 4-bit in-memory digital comparator with AMT RRAMs, which can replace five logic gates in a traditional approach. The AMT-based digital comparator may open the door for future integrated functions and applications in optoelectronic data storage and simplify the complex logic operations.

Inspired by the parallel performing of storage and processing information in biological synapse, artificial synapse that can control ionic and protonic currents are ideal means for attracted tremendous attention as a promising option to break the von Neumann bottleneck. Here we demonstrate an artificial synapse with tunable synaptic behavior based on solution-processed two-dimensional (2D) C3N/polyvinylpyrrolidone (PVPy). The proton modulated memristive characteristics of synaptic device are verified by ambient pressure X-ray photoelectron spectroscopy under different H2O gas atmosphere. The highly proton conducting of C3N/PVPy matrix stems from the hydrogen bonding network between C3N and PVPy, as well as the large amount of ordered nitrogen atoms in C3N. The artificial synapse ensure a direct imitation of short-term and long-term plasticity in biological synapses including excitatory post-synaptic current (EPSC), paired-pulse facilitation (PPF), paired-pulse depression (PPD), PPF following PPD and post-tetanic potentiation (PTP). The C3N/PVPy matrix-based memristor which can mimic the synapse cleft based on proton conducting mechanism may find further applications in artificial intelligence.

The coherent developments of high performance broadband photodetection and a discrimination technique are highly essential for multiscene imaging and optical communication applications. The integration of traditional bandpass filters or stacking other spectral absorber in photodetectors often complicates the device design and leads to asymmetric photogain for each waveband. Herein, we report on ultraviolet–visible (UV-vis) multispectral photodetection based on a single ZnO nanowire (NW) phototransistor, where defect reconstructions can be reliably induced by a two-step annealing that leads to the observed broadband photodetection. Electron paramagnetic resonance and photoluminescence spectra reveal the reconstructions of zinc-atom-related defects (i.e., zinc interstitials and vacancies). Combined microdifferential reflectance and multimode scanning probe microscope (SPM) technique confirm the presence of a unique visible-sensitive Zn-rich ZnO shell layer and a trap-free UV-sensitive ZnO core. We achieve n...

MXenes have drawn considerable attention in both academia and industry due to their attractive properties, such as a combination of metallic conductivity and surface hydrophilicity. However, to the best of our knowledge, the potential use of MXenes in non-volatile resistive random access memories (RRAMs) has rarely been reported. In this paper, we first demonstrated a RRAM device with MXene (Ti3C2) as the active component. The Ti3C2-based RRAM exhibited typical bipolar switching behavior, long retention characteristics, low SET voltage, good mechanical stability and excellent reliability. By adjusting different compliance currents in the SET process, multi-state information storage was achieved. The charge trapping assisting hopping process is considered to be the main mechanism of resistive switching for this fabricated Ti3C2-based RRAM, which was verified by conductive atomic force microscopy (C-AFM) and Kelvin probe force microscopy (KPFM). Moreover, this flexible Ti3C2-based RRAM, with good mechanical stability and long retention properties, was successfully fabricated on a plastic substrate. Ti3C2-based RRAMs may open the door to additional applications and functionalities, with high potential for application in flexible electronics.

Mimicking biological synapses with resistive random switching memory (RRAM) can lay a concrete foundation for the future of artificial intelligence. RRAMs based on low-cost and solution-processed organic materials provide a competitive approach. Here, we report an artificial synaptic device with a solution-processed small molecule (bis-4-(N-carbazolyl)phenyl)phenylphosphine oxide (BCPO) based RRAM. The BCPO-based RRAM exhibits reproducible resistive switching behavior, a long retention time as well as good thermal tolerance with a sufficient on/off current ratio. In situ Kelvin probe force microscopy (KPFM), conductive atomic force microscopy (C-AFM) and density function theory (DFT) calculations demonstrate that both the redox state and the trap-filled space charge limited current (SCLC) determine the resistive switching of a BCPO-based RRAM. Furthermore, the fabricated device was employed to emulate a biological synapse in which several synaptic functions, including spike-rate-dependent plasticity (SRDP), a transition from short-term plasticity (STP) to long-term plasticity (LTP) and spike-time-dependent plasticity (STDP), were realized. To the best of our knowledge, this is the first successful demonstration of solution-processed small molecules in artificial synaptic devices. The BCPO layer is solution-processed at low temperature which is compatible with a flexible substrate and printable electronics. We believe this electronic synapse will have a wide range of applications in future neuromorphic computing.

Understanding the mobility and electrochemical process of ion species at the nanoscale is the key step to obtain high performance non-volatile data storage devices. Here, we present a systematic study on the resistive switching mechanism of an assembled metal–oxo cluster [Bi6O4(OH)4](NO3)6 (BiON) to build reliable non-volatile memory devices. The bipolar resistive-switching with low operation voltage is observed and is attributed to the migration of ion species (mainly OH−) with low activation energy (∼0.20 eV) along the polar electric field and simultaneous valence change of the bismuth of the [Bi6O4(OH)4] clusters in the BiON film. The X-ray photoelectron spectroscopy (XPS), conductive atomic force microscopy (CAFM) and Kelvin probe force microscopy (KPFM) characterization confirms that OH− with low activation energy is expected to facilitate the formation and rupture of anion vacancy filaments. Moreover, intermediate oxide layers formed at the Al/BiON interfaces could help to stabilize and assist the resistive switching. The control of the clusters and counter-ion species in the metal–oxo cluster-assembled materials represents a step toward next generation multi-level, high-speed and low energy consumption non-volatile memory devices for in-memory computing and artificial intelligence.

Redox-based nonvolatile memory, where the nanoscale redox process is generally conceived to account for resistance change in external electrical stimuli, is attracting much scientific interest because of its high potential for application in next-generation memory and neuromorphic computing systems. However, materials used in current redox-based resistive switching memory are usually restricted by bulk transition metal oxides. Polyoxometalates (POMs)—a class of discrete early transition metal oxide molecular clusters—are gaining popularity in emerging applications of molecular electronic devices owing to their nanoscale size, high stability, and rich reversible redox potential. This paper reports nonvolatile memory associated with the redox behavior of POMs. Resistive switching mechanisms of POM-based resistive switching memory were systematically investigated for the first time. The intrinsic redox property of POMs was confirmed to initiate the formation of conductive filaments, which are developed by the migration of oxygen ions, as verified through electron energy loss spectroscopy. The results of this study could provide valuable insight into application of redox molecular-based memory for high-density data storage.

Resistive random-access memory (RRAM) is the most promising research direction of the next generation non-volatile memory (NVM) devices, and seeking novel materials as the active layer can facilitate RRAM device development. Two-dimensional graphitic carbon nitride (g-C3N4) nanosheets with a mass of carrier trapping sites are employed as the active layer of RRAM devices. Solution processed memory devices show good stability and reliability, and the fabricated g-C3N4-based RRAM devices show a non-volatile behavior and a bipolar switching characteristic with an ON/OFF ratio of 103, a low operation voltage and good retention capability. In addition, by means of controlling the compliance current precisely, multi-level data storage can be realized. The mechanism of the RRAM devices is thought to be carrier trapping assisted hopping, which is verified by atomic force microscopy in the electrical mode. This work demonstrates that g-C3N4 nanosheet based RRAM devices provide a novel direction for low energy and high density data storage devices.

Neuromorphic computing could tackle the inherent limitations of traditional von Neumann architecture in devoted machine learning applications. Nevertheless, implementation of a transistor-based artificial synapse which is the fundamental building block for mimicking the functions of biological synapses remains challenging owing to the nonlinear and asymmetric weight update protocol and fast saturation within the initial few pulses. Here, a three-terminal photoactive synapse is proposed based on black phosphorus (BP)–ZnO hybrid nanoparticles (NPs) with a combination of an electronic mode and a photoactive mode. By the electronic mode, the channel conductance can be manipulated by charge trapping inside the thin BP–ZnO NPs which ensures an enlarged variation margin and the realization of 4 synaptic weight levels, while optical modulation of the excitatory and inhibitory synaptic weights with symmetry and linearity variation was realized, since an extended energy threshold of photon absorption and accelerated dissipation of excitons can be achieved in the BP–ZnO hybrid NPs. Crucially, we can modulate the synaptic weight simply by varying the wavelength of the light source (365, 520, and 660 nm) and achieve an extended synaptic weight change of 400% within 10 optical pulses. This study proposes an extremely simple and powerful system with multiple forms of synaptic plasticity resembling an analogue of a natural biological synapse due to the broadband response of the BP–ZnO hybrid NPs.

The introduction of the “Internet of Things” (IoTs) concept has spawned a series of research and development of wearable sensors. Flexible field‐effect transistors (FETs) are considered to be potential sensing devices due to the variety of material utilization and the self‐amplifying function on electrical signals. FETs have demonstrated the ability of detecting different kinds of external stimuli and continuous monitoring functionalities. Herein, the recent progress achieved by the academia in wearable sensors based on flexible FETs, including pressure, temperature, chemical, and biological analytes, which are vital for the manufacturing of smart wearable devices, is summarized. The sensing mechanism for different sensors is introduced and an in‐depth discussion is presented, including material engineering, problems at the current stage, and future challenges.

Core-shell semiconductor quantum dots (QDs) are one of the biggest nanotechnology successes so far. In particular, type-I QDs with straddling band offset possess the ability to enhance the charge carriers capturing which is useful for memory application. Here, the type-I core-shell QD-based bipolar resistive switching (RS) memory with anomalous multiple SET and RESET processes was demonstrated. The synergy and competition between space charge limited current conduction (arising from charge trapping in potential well of type-I QDs) and electrochemical metallization (ECM, originating from redox reaction of Ag electrode) process were employed for modulating the RS behavior. Through utilizing stochastic RS mechanisms in QD-based devices, four situations of RS behaviors can be classified into three states in Markov chain for implementing the application of a true random number generator. Furthermore, a 6 × 6 cross-bar array was demonstrated to realize the generation of random letters with case distinction.

To keep pace with the upcoming big-data era, the development of a device-level neuromorphic system with highly efficient computing paradigms is underway with numerous attempts. Synaptic transistors based on an all-solution processing method have received growing interest as building blocks for neuromorphic computing based on spikes. Here, we propose and experimentally demonstrated the dual operation mode in poly{2,2-(2,5-bis(2-octyldodecyl)-3,6-dioxo-2,3,5,6-tetrahydropyrrolo[3,4-c]pyrrole-1,4-diyl)dithieno[3,2-b]thiophene-5,5-diyl-alt-thiophen-2,5-diyl}(PDPPBTT)/ZnO junction-based synaptic transistor from ambipolar charge-trapping mechanism to analog the spiking interfere with synaptic plasticity. The heterojunction formed by PDPPBTT and ZnO layers serves as the basis for hole-enhancement and electron-enhancement modes of the synaptic transistor. Distinctive synaptic responses of paired-pulse facilitation (PPF) and paired-pulse depression (PPD) were configured to achieve the training/recognition function for digit image patterns at the device-to-system level. The experimental results indicate the potential application of the ambipolar transistor in future neuromorphic intelligent systems.

In recent years, halide perovskite quantum dots (HP-QDs) based composites have been widely developed and used in various applications owing to their unique photonic, electronic and mechanical properties, as well as high stability to water, oxygen, heat and illumination. Remarkable efforts have been made in the synthesis and applications of these materials in photonics, electronics, sensors and other fields. Besides these topics, we also cover enhancement of optoelectronic properties as well as chemical, thermal and photostability of HP-QDs-based composites. We hope this review will promote both the development and applications of perovskite-based materials., GRAPHICAL ABSTRACT 10.1080/14686996.2020.1752115-UF0001

Memory device is the important component in the electronic equipment. The electrical properties of three-terminal optoelectronic memory device can be regulated not only by the electrical operation but also by the light illumination. In this chapter, we firstly introduce the three-terminal optoelectronic memory device and the application on various electronic devices. Secondly, since the semiconductor layers or functional layers of three-terminal optoelectronic memory device are generally sensitive to the light which can change the electrical properties, the working mechanism will be described. Thirdly, the development and the representational work will be presented for three-terminal optoelectronic memory device. Finally, the flexible three-terminal optoelectronic memory devices will be also described.

The field of perovskite halides has made great strides and perovskites become an interesting platform for building optoelectronic devices, such as solar cell, light-emitting diodes (LEDs), and phototunable memories. In devices that rely on light-induced charge-trapping performance, perovskite halides have significant capacity for promoting tunable high-density data storage devices. Moreover, the ionotropic effects in perovskite halides make them promising candidates for constructing artificial synapse with the capabilities of concurrent processing and learning thanks to the close electronic–ionic coupling. Here, we summarize the recent burgeoning of the device application of perovskite halides with an emphasis on phototunable properties. The understanding of perovskite halides in neuromorphic computing has moreover predicted foreseeable promising prospects and obstacles in this remarkable field.

Natural biomaterials are capable of producing biodegradable and/or biocompatible electronic devices for vast applications such as next-generation green data storage. Here, we demonstrate an environment-friendly alkali lignin as a favourable candidate for resistive switching memory storage applications. The resistive memory devices based on gold nanoparticles embedded alkali lignin (Au NPs:lignin) exhibits a typical write-once-read-many-times memory (WORM) resistive switching behaviour with a large on-off ratio (>104), and long data retention characteristics (>103 s) under low power operation (4.7 V). Au NPs acting as trapping sites in the active layer are responsible for the resistive switching mechanism. The active layer is fabricated on organic substrates through facile solution-processed methods under normal ambient conditions. The skin-inspired polylactide (PLA) film is utilized as an ultrathin substrate endowing the memory devices with good mechanical support and biodegradability. This work opens up new avenues towards renewable and environmentally benign lignin-based materials for biodegradable electronic devices.

Interface engineering has been recognized to be substantially critical for achieving efficient charge separation, charge carrier transport, and enhanced device performance in emerging optoelectronics. Nevertheless, precise control of the interface structure using current techniques remains a formidable challenge. Herein, we demonstrate a facile and versatile protocol wherein in situ thiol–ene click photopolymerization-induced phase separation is implemented for constructing heterojunction semiconductor interfaces. This approach generates continuous mountainlike heterojunction interfaces that favor efficient exciton dissociation at the interface while providing a continuous conductive area for hole transport above the interface. This facile low-temperature paradigm presents good adaptability to both rigid and flexible substrates, offering high-performance UV-responsive phototransistors with a normalized detectivity up to 6.3 × 1014 cm Hz1/2 W–1 (also called jones). Control experiments based on ex situ phot...

Motivated by the biological neuromorphic system with a high degree of connectivity to process huge amounts of information, light-triggered electronic synapses are expected to pave the way for overcoming the von Neumann bottleneck for non-conventional computing. Here, a light-triggered electronic synapse based on solution-processed ZnO–phosphorene nanoparticles (ZP NPs) with an in situ fabricated heterojunction is demonstrated. In situ Kelvin probe force microscopy (KPFM) and conductive atomic force microscopy (CAFM) have been employed to investigate the operation principle of the memristor. The essential synaptic functions of learning and memory of the bio-synapse have been emulated, including potentiation and depression under positive and negative pulse trains, paired-pulse facilitation (PPF), spike-rate-dependent plasticity (SRDP), spike-timing-dependent plasticity (STDP) and short-term potentiation (STP) to long-term potentiation transition (LTP). The well-arranged pn junction inside the ZP NPs arouses the multiple wavelength response and fast separation of photogenerated excitons, which serves as the basis for broadband optically-mediated plasticity. The experimental results confirm that ZP nanoparticles could be promising key components for mimicking biological synaptic behaviors with light stimulation, which is of great importance for the further development of artificial light triggered neural systems.

Digital technology is one of the greatest modern breakthroughs, allowing sounds, words and images to be stored in binary form. However, there is a huge gap between the amount of data created daily and the capacities of existing storage media. Developing multibit memory in which 2n levels, typically represented by distinguishable current levels, can be achieved in a single cell is a critical specification for achieving high-density memory devices. Compared with electrically operated memory, photonic memory—in which electrical read-out is orthogonal to the photo-programming operation—promises high differentiation among different data levels. From another aspect, benefiting from its high density, multifunctionality, low power consumption, and multilevel data storage, photonic memory devices hold future promise for built-in, non-volatile memory and reconstructed logic operation and are expected to bridge this capacity gap. Thus, we present a review on the development of photonic memory, with a view towards inspiring more intriguing ideas on the elegant selection of materials and design of novel device structures that may finally induce major progress in the manufacture and application of photonic memory.

Since the discovery of perovskite materials with the general chemical formula ABX3 in 1839, research on perovskite materials has drawn exponentially greater attention, owing to their flexible properties, including their ferroelectric, piezoelectric, and dielectric, as well as resistive switching, properties. Perovskite materials have high potential for developing nonvolatile high-density data storage devices. In this review, we summarized the current development of the application of perovskites in nonvolatile memories with an emphasis on the resistive switching properties. The unique advances of perovskite materials are introduced, followed by an assortment of the characterisations of their compositions and crystal structures. The insight into perovskite materials in artificial synapses has also concluded with predictable opportunities and challenges in this promising field.

The computing based on artificial neuron network is expected to break through the von Neumann bottleneck of traditional computer, and to greatly improve the computing efficiency, displaying a broad prospect in the application of artificial visual system. In the specific structural layout, it is a common method to connect the discrete photodetector with the artificial neuron in series, which enhances the complexity of signal recognition, conversion and storage. In this work, organic small molecule IR-780 iodide is inserted into the memory device as both the charge trapping layer and near-infrared (NIR) photoresponsive film. Through electrical and optical regulation, artificial synaptic functions including short-term plasticity, long-term plasticity, and spike rate dependence are realized. In the established artificial sensory neuron system, NIR optical pulses can significantly improve the spiking rate. Moreover, the spiking neural networks are further constructed by simulation for handwritten digit classification. This research may contribute to the development of light driven neural robots, optical signal encryption, and neural computing.

In the past several years, two-dimensional black phosphorus (BP) has captured the research community's interest because of its unique electronic, photonic, and mechanical properties. Remarkable efforts have been made regarding the synthesis, fundamental understanding, and applications of BP in the fields of nanoelectronics, nanophotonics, and optoelectronics. In this review, we summarize the recent developments in the study of BP, which covers the state-of-the-art synthesis methods for preparing single-layer or few-layer BP, the recent advances in characterizing its electronic, optical and mechanical properties, and the reported functional devices utilizing such properties. Finally we discuss the existing challenges in developing BP-based nanoelectronics and optoelectronics, and describe the prospects for future BP-related research.

In article number 1900213, Ye Zhou, Su‐Ting Han, and co‐workers demonstrate a photo‐induced transition from oxygen vacancy filament to metal filament in the heterostructure of titanium oxides/graphene. A voltage controlled negative differential resistance effect is also obtained. This will provide significant insight into the design and manipulation of memristive devices based on heterostructured materials.

Depending on the mobile species involved in the resistive switching process, redox random access memories and conductive bridge random access memories are widely studied with distinct switching mechanisms. Although the two resistance switching types have faithfully proved to be electrochemically linked in metal oxide‐based memristive devices, the corresponding photo‐induced transition has not yet been realized. Here, a photo‐induced transition through the integration of a graphene layer into a titanium oxide‐based memory device is demonstrated. Coupled with Raman mapping and an electron energy loss spectroscopy technique, the photo‐induced interaction at the heterostructure of graphene/titanium oxide are considered to dominate the transition process. Moreover, a negative differential resistance effect is observed by controlling the applied voltage, which can be credited to the saturation of trap centers (oxygen vacancies) and the increase of interfacial barrier at the graphene/titanium oxide heterojunction.

Increasing environmental crises caused by exploring and using fossil fuels have compelled human being to develop innovative technologies to utilize renewable and sustainable energy sources. For this purpose, photovoltaic conversion of solar energy into electricity with solar cells is a promising and attracting way in that solar energy is clean and inexhaustible. Nowadays, the bottleneck in the application of solar cells on a large scale to sustainable energy generation still lies in lacking an efficient, stable and low-cost materials system for photon-to-electricity conversion. Perovskite materials are a class of materials widely applied in solar cells. Many evidences showed that the perovskite materials have both ferroelectric and photovoltaic properties, offering a special system called photoferroelectric materials. A built-in electric field established in these materials due to the ferroelectric property is more helpful for the separation of e-h pairs and enhancing the power conversion efficiency during photovoltaic process in solar cells. Here, we review the recent photoferroelectric perovskite solar cells (PPSCs). After giving a brief description of the structure and property of photoferroelectric perovskite materials, the device structures, working principles and characterization of PPSCs are introduced, followed by the state-of-the-art advances and the insights for the PPSCs based on oxide and halide perovskite materials. Finally, the main challenges in developing efficient PPSCs are discussed.

Electronic synapses implementing in-memory computing system could overcome the developing limitation on the energy efficiency of traditional von Neumann architecture. Compared with the high sensitivity of biological synapses, lower responsivity of the memristive synapses was found via the electrical stimulations. Here, poly{2,2-(2,5-bis(2-octyldodecyl)-3,6-dioxo-2,3,5,6- tetrahydropyrrolo[3,4-c]pyrrole-1,4-diyl)-dithieno[3,2-b]thiophene-5,5-diyl-alt-thiophen-2,5-diyl} (PDPPBTT)/zinc oxide (ZnO) based heterojunction is found to exhibit stable memristive switching behavior, which originates from the confined formation/rupture of filament in the two-layer interface region as the ions migrate with different transport rates in two layers. The implementing synaptic functions in the sensitive memristive device can realize the short-term plasticity and long-term plasticity when stimulated by the applied electrical signals with different stimulating rate. Similar to the biological synapse, the memory loss, memory transition, and the critical role of stimulation rate on the transition process, can be achieved in the as-prepared memristor device. The systematic demonstrations on the synaptic emulation may facilitate building bio-inspired device-level neuromorphic systems.

Previous investigations on rare-earth oxides (REOs) reveal their high possibility as dielectric films in electronic devices, while complicated physical methods impede their developments and applications. Herein, we report a facile route to fabricate 16 REOs thin insulating films through a general solution process and their applications in low-voltage thin-film transistors as dielectrics. The formation and properties of REOs thin films are analyzed by atomic force microscopy (AFM), X-ray diffraction (XRD), spectroscopic ellipsometry, water contact angle measurement, X-ray photoemission spectroscopy (XPS), and electrical characterizations, respectively. Ultrasmooth, amorphous, and hydrophilic REO films with thickness around 10 nm have been obtained through a combined spin-coating and postannealing method. The compositional analysis results reveal the formation of RE hydrocarbonates on the surface and silicates at the interface of REOs films annealed on Si substrate. The dielectric properties of REO films are investigated by characterizing capacitors with a Si/Ln

Field-effect transistors (FETs) employing the solution-processed metal oxides as dielectrics have shown excellent performance on rigid silicon substrate. However, there have been very limited reports on FETs with solution-processed, thermal-annealed metal oxide dielectrics on low-cost flexible substrates. To date, to our knowledge there is almost no report on flexible FETs having the similar or better performance than the FETs on Si substrate with solution-processed, thermal-annealed metal oxides as dielectrics. Herein, flexible pentacene-based organic FETs have been demonstrated with polystyrene (PS)-modified, solution-processed yttrium oxide (Y2O3), hafnium oxide (HfO2), or aluminum oxide (Al2O3) as dielectrics on commercially available aluminum (Al) foil substrates. The transistors exhibit a good carrier mobility (µ), a large on/off current ratio (Ion/off), a low threshold voltage (VT), and a small subthreshold swing (SS) at a low-operation voltage (−4 V), which is comparable with the device performance on rigid Si substrate. Therefore, the excellent electrical performance of the FETs on commercially available Al foil substrate, together with the virtue of light-weight, makes the devices possess the promising potential for various electronic system applications.